Users’ Departure Time Prediction Based on Light Gradient Boosting Decision Tree

Wireless Algorithms, Systems, and Applications (WASA 2022)

Lingyu Zhang 1,2    Zhijie He 2    Xiao Wang2    Ying Zhang2    Jian Liang2    Guobin Wu2    Ziqiang Yu3    Penghui Zhang 4    Minghao Ji4    Pengfei Xu4    Yunhai Wang1   

1School of Computer Science and Technology, Shandong University, Qingdao, China   
2Didi Chuxing, Beijing, China   
3Yantai University, Yantai, China   
4School of Information Science and Technology, Northwest University, Kirkland, USA   


Abstract

With the development of urban transportation networks, the flow of people in cities generally shows the characteristics of concentration, periodicity and irregularity, and a typical example is rush hour. For most existing taxi-hailing apps, users frequently queue up for a relatively long time during rush hour and may even fail to get orders taken due to various factors. To solve this problem, we propose a users’ departure time prediction model based on Light Gradient Boosting Machine (TP-LightGBM), which will remind users to book taxis before their journeys. As we know, TP-LightGBM may be the first model for departure time prediction. We uncover that travel behavior patterns vary under different external conditions through statistics and analysis of users’ historical orders from multiple perspectives. Furthermore, we extract multiple features from these orders and select the favorable features by calculating their information gain as the input of TP-LightGBM to predict users’ departure time. Therefore, our model can provide users with the recommendations of the best departure time if they need them. The final experimental results on our datasets indicate that TP-LightGBM has more excellent performance with great stability in predicting user departure time than other baseline models.

Paper: [PDF]        

Bibtex

@inproceedings{zhang2022users,
  title={Users’ Departure Time Prediction Based on Light Gradient Boosting Decision Tree},
  author={Zhang, Lingyu and He, Zhijie and Wang, Xiao and Zhang, Ying and Liang, Jian and Wu, Guobin and Yu, Ziqiang and Zhang, Penghui and Ji, Minghao and Xu, Pengfei and others},
  booktitle={International Conference on Wireless Algorithms, Systems, and Applications},
  pages={595--605},
  year={2022},
  organization={Springer}
}